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  To handle these phenomena, we suggest a Dialogue State Tracking with
  Slot Connections (DST-SC) model to explicitly consider slot correlations across completely different
  domains. Specially, we first apply a Slot Attention to study a set of slot-particular options from the unique dialogue
  after which integrate them using a slot information sharing module.
  Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang creator Yi
  Guo writer Siqi Zhu writer 2020-nov textual content Proceedings of the 2020 Conference on Empirical Methods in Natural
  Language Processing (EMNLP) Association for Computational Linguistics Online convention publication Incompleteness
  of domain ontology and unavailability of some values are two inevitable issues of dialogue
  state tracking (DST). In this paper, we suggest a new architecture
  to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), known as SAVN.
  SAS: Dialogue State Tracking through Slot Attention and Slot Information Sharing
  Jiaying Hu creator Yan Yang writer Chencai Chen author Liang He author Zhou
  Yu creator 2020-jul textual content Proceedings of the 58th Annual
  Meeting of the Association for Computational Linguistics Association for Computational Linguistics
  Online convention publication Dialogue state tracker is accountable for inferring user intentions
  by way of dialogue history. We propose a Dialogue State Tracker with Slot Attention and
  Slot Information Sharing (SAS) to scale back redundant information’s interference and improve long
  dialogue context tracking.